# Chaotic Variational Auto Encoder based One Class Classifier for Insurance Fraud Detection

arXiv: https://arxiv.org/abs/2212.07802 · HTML: https://mlsanity.com/paper/2212.07802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [Feature subset selection for Big Data via Chaotic Binary Differential Evolution under Apache Spark](https://mlsanity.com/paper/2202.03795/) (2022)
- [ATM Cash demand forecasting in an Indian Bank with chaos and deep learning](https://mlsanity.com/paper/2008.10365/) (2020)

## Built on by (most cited first, 2 of 2)

- [Chaotic Variational Auto encoder-based Adversarial Machine Learning](https://mlsanity.com/paper/2302.12959/) (2023)
- [Bi-Level Chaotic Fusion Based Graph Convolutional Network for Stock Market Prediction Interval](https://mlsanity.com/paper/2605.16324/) (2026)

## Abstract (arXiv)

Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insurance and auto insurance among other things. In either of the categories, the fraud detection techniques should be designed in such a way that they capture as many fraudulent transactions as possible. Owing to the rarity of fraudulent transactions, in this paper, we propose a chaotic variational autoencoder (C-VAE to perform one-class classification (OCC) on genuine transactions. Here, we employed the logistic chaotic map to generate random noise in the latent space. The effectiveness of C-VAE is demonstrated on the health insurance fraud and auto insurance datasets. We considered vanilla Variational Auto Encoder (VAE) as the baseline. It is observed that C-VAE outperformed VAE in both datasets. C-VAE achieved a classification rate of 77.9% and 87.25% in health and automobile insurance datasets respectively. Further, the t-test conducted at 1% level of significance and 18 degrees of freedom infers that C-VAE is statistically significant than the VAE.